code: def deepspeed_launcher(args):
      import torch.distributed.run as distrib_run
  
      if not is_deepspeed_available():
          raise ImportError(
              "DeepSpeed is not installed => run `pip3 install deepspeed` or build it from source."
          )
  
      cmd, current_env = prepare_deepspeed_cmd_env(args)
      if not check_cuda_p2p_ib_support():
          message = "Using RTX 4000 series which doesn't support faster communication speedups. Ensuring P2P and IB communications are disabled."
          warn = False
          if "NCCL_P2P_DISABLE" not in current_env:
              current_env["NCCL_P2P_DISABLE"] = "1"
              warn = True
          if "NCCL_IB_DISABLE" not in current_env:
              current_env["NCCL_IB_DISABLE"] = "1"
              warn = True
          if warn:
              logger.warning(message)
  
      if (
          args.num_machines > 1
          and args.deepspeed_multinode_launcher
          != DEEPSPEED_MULTINODE_LAUNCHERS[1]
      ):
          with open(".deepspeed_env", "a") as f:
              for key, value in current_env.items():
                  if ";" in value or " " in value:
                      continue
                  f.write(f"{key}={value}\n")
  
          process = subprocess.Popen(cmd, env=current_env)
          process.wait()
          if process.returncode != 0:
              if not args.quiet:
                  raise subprocess.CalledProcessError(
                      returncode=process.returncode, cmd=cmd
                  )
              else:
                  sys.exit(1)
      else:
          debug = getattr(args, "debug", False)
          args = _filter_args(
              args,
              distrib_run.get_args_parser(),
              [
                  "--training_script",
                  args.training_script,
                  "--training_script_args",
                  args.training_script_args,
              ],
          )
          with patch_environment(**current_env):
              try:
                  distrib_run.run(args)
              except Exception:
                  if is_rich_available() and debug:
                      console = get_console()
                      console.print(
                          "\n[bold red]Using --debug, `torch.distributed` Stack Trace:[/bold red]"
                      )
                      console.print_exception(
                          suppress=[__file__], show_locals=False
                      )
                  else:
                      raise
  
imports: import argparse
  import importlib
  import logging
  import os
  import subprocess
  import sys
  from pathlib import Path
  import psutil
  import torch
  from accelerate.commands.config import default_config_file, load_config_from_file
  from accelerate.commands.config.config_args import SageMakerConfig
  from accelerate.commands.config.config_utils import DYNAMO_BACKENDS
  from accelerate.state import get_int_from_env
  from accelerate.utils import ComputeEnvironment, DistributedType, PrepareForLaunch, _filter_args, check_cuda_p2p_ib_support, is_bf16_available, is_deepspeed_available, is_npu_available, is_rich_available, is_sagemaker_available, is_torch_version, is_tpu_available, is_xpu_available, patch_environment, prepare_deepspeed_cmd_env, prepare_multi_gpu_env, prepare_sagemager_args_inputs, prepare_simple_launcher_cmd_env, prepare_tpu
  from accelerate.utils.constants import DEEPSPEED_MULTINODE_LAUNCHERS, TORCH_DYNAMO_MODES
label_desc: Use image node_id 5 for calling a global function with example usage: deepspeed_launcher(args) without return types